The accurate detection of disease clusters is a well acknowledged problem. False detections may be expected under repetitive testing, but the opposite situation, that of failing to detect clusters when they actually exist, is increasingly recognized. Both false detections and failures-to-detect have striking implications for epidemiological studies, since space-time clustering is often a first step in the analysis of suspected public health problems. The failure to find existing clusters means that real disease clusters will be ignored, but false detections result in a loss of public confidence and wasted resources. Clearly, studies are required to accurately determine the abilities of space-time clustering methods under different spatial and temporal patterns of disease incidence. The objective of this project is (1) to determine the ability of existing methods to detect cancer clusters, and (2) to develop novel, sensitive approaches to the detection of disease clusters in situations where environmental and geographic causes (such as proximity to power lines or nuclear fuel processing facilities) are suspected. The purpose of the phase I research is to develop a user-friendly, interactive, PC program CANCLUS. The program serves two purposes: Cluster description through interactive mapping and graphics and cluster detection using sophisticated statistical techniques. CANCLUS thus provides a much needed software tool to be used by academe, government and private industry in the investigation of cancer and disease clusters. CANCLUS will serve as a basis for the phase II research, which will determine the sensitivity of existing cluster detection techniques and develop new cluster detection methodologies.